- Research article
- Open Access
Molecular, genetic and transcriptional evidence for a role of VvAGL11 in stenospermocarpic seedlessness in grapevine
© Mejía et al; licensee BioMed Central Ltd. 2011
- Received: 23 July 2010
- Accepted: 29 March 2011
- Published: 29 March 2011
Stenospermocarpy is a mechanism through which certain genotypes of Vitis vinifera L. such as Sultanina produce berries with seeds reduced in size. Stenospermocarpy has not yet been characterized at the molecular level.
Genetic and physical maps were integrated with the public genomic sequence of Vitis vinifera L. to improve QTL analysis for seedlessness and berry size in experimental progeny derived from a cross of two seedless genotypes. Major QTLs co-positioning for both traits on chromosome 18 defined a 92-kb confidence interval. Functional information from model species including Vitis suggested that VvAGL11, included in this confidence interval, might be the main positional candidate gene responsible for seed and berry development.
Characterization of VvAGL11 at the sequence level in the experimental progeny identified several SNPs and INDELs in both regulatory and coding regions. In association analyses performed over three seasons, these SNPs and INDELs explained up to 78% and 44% of the phenotypic variation in seed and berry weight, respectively. Moreover, genetic experiments indicated that the regulatory region has a larger effect on the phenotype than the coding region. Transcriptional analysis lent additional support to the putative role of VvAGL11's regulatory region, as its expression is abolished in seedless genotypes at key stages of seed development. These results transform VvAGL11 into a functional candidate gene for further analyses based on genetic transformation.
For breeding purposes, intragenic markers were tested individually for marker assisted selection, and the best markers were those closest to the transcription start site.
We propose that VvAGL11 is the major functional candidate gene for seedlessness, and we provide experimental evidence suggesting that the seedless phenotype might be caused by variations in its promoter region. Current knowledge of the function of its orthologous genes, its expression profile in Vitis varieties and the strong association between its sequence variation and the degree of seedlessness together indicate that the D-lineage MADS-box gene VvAGL11 corresponds to the Seed Development Inhibitor locus described earlier as a major locus for seedlessness. These results provide new hypotheses for further investigations of the molecular mechanisms involved in seed and berry development.
- Berry Development
- Positional Candidate Gene
- Pinot Noir
- Berry Weight
- Berry Size
Vitis vinifera L genomic resources, including both released genomic sequences [1, 2], allow the characterization at molecular level of the biological function of genes involved in agronomically interesting traits [3–6]. Stenospermocarpic seedlessness , found in popular table grape varieties for fresh or dried consumption such as Sultanina (Thompson Seedless), is one of these traits. In stenospermocarpic berries, pollination and fertilization occur but both the seed coat and endosperm cease their normal development at early stages, leaving undeveloped seeds or seed traces [7, 8].
Seed and berry size depend on genetic background, and they both segregate in experimental populations with a continuous distribution indicative of polygenic determinism [8–11]. To increase the chances of obtaining new seedless varieties, breeding programs commonly cross two seedless parental genotypes and progeny are obtained through embryo rescue assisted by in vitro tissue culture . The progeny thus obtained (n < 200 in general) are used to investigate the genetic basis of grape seedlessness and berry size [4, 9–11, 13–17]. The most accepted model proposed that genetic inheritance of seedlessness in grapevine is based on the expression of three independent recessive genes under the control of a dominant regulator gene named SDI (Seed Development Inhibitor) [10, 13, 14, 18]. This model was partly confirmed by several studies that all reported a major QTL for seedlessness co-localizing with SDI on linkage group (LG) 18. This major QTL explains 50% to 70% of the phenotypic variation of the trait [4, 9, 10, 15, 16]. Numerous other minor QTLs were found on different LGs, but they were not reproducible across different seasons and were not present in all crosses. Thus, the molecular characterization of the SDI locus is a key step toward understanding the molecular mechanisms underlying seedlessness.
In Arabidopsis and other model species, genes involved in flower, ovule, seed and fruit development have been isolated and characterized from loss of function mutants. Among them, the MADS-box family plays an important role . Most of the MADS-box genes identified in Arabidopsis seem to have counterparts in grapevine . In spite of grapevine particular features, characterized MADS-box genes expressed during the reproductive development might have the same role than their functionally characterized orthologues in model species . Among these MADS-box genes, VvAGL11 (VvMADS5 , VvAG3 ) shows homology to the STK/AGL11 gene in Arabidopsis and is expressed in mature carpels, developing seeds and pre- and post véraison fruits; this expression suggests a possible role for this gene in ovule, seed and berry development in grapevine . VvAGL11 was also mapped in silico to the same contig that contains the SDI locus and the closest marker to a seedlessness QTL (SSR VMC7F2 ), suggesting that it might play an important role in seed development. In parallel, a transcriptional analysis of genes differentially expressed in the flowers of seeded and seedless Sultanina lines allowed the identification of a chloroplast chaperonin (ch-Cpn21) whose silencing in tobacco and tomato resulted in seed abortion , and of a ubiquitin extension protein (S27a) having a probable general role in the control of organ development in grapevine . None of these genes co-segregated with the SDI locus. Besides these works, no further evidence has been generated to unveil the genetic control of seedlessness in grapevine.
Genetic analyses have also revealed a major QTL for berry size [4, 9, 10, 16] and ripening date [4, 10, 16] that overlap with the major seedlessness QTL on LG 18. The complex developmental process modified by genetic, physiological and environmental factors that underlies berry development was first reviewed by Coombe  and was very recently updated by Carmona et al. . The relationship between seed number and berry size was reviewed by Ollat et al. . These overlapping QTLs detected by genetic experiments could be reflective of pleiotropic effects caused by hormones in developing seeds [9, 16]. However, most of the phenotypic variation for berry size is not explained by the SDI locus [9, 10, 16], and there is still room for the identification of other loci involved in seed and berry development. The molecular biology of fleshy fruit ripening has received considerable attention [26, 27], but little is known about the determinants of early fleshy fruit morphogenesis. Differential screening of ESTs and berry transcriptomic analysis identified several genes that show differential expression during young fruit development, the onset of véraison and ripening [26, 28–31].
In this work, we designed a strategy to test the hypothesis for a possible role of VvAGL11 in seeddlessness. We integrated multiple genomic resources as soon as they became available to contribute to the molecular characterization of the SDI locus: QTL mapping in seedless × seedless derived progeny , physical mapping on a Cabernet-Sauvignon physical map  and the released sequence of grapevine , which gave further positional evidence for VvAGL11 as being the major gene responsible for seedlessness . Here, we provide genetic and transcriptional support for this hypothesis and discuss its potential for molecular-assisted breeding programs.
Phenotypic evaluations of plants grown in their own roots (2007 season) and over Sultanina rootstocks (2009 and 2010 seasons) confirmed the distribution of seed and berry weight previously reported by Mejía et al.  for the same progeny (Additional file 1). Neither of the two traits fit a normal distribution (P-value < 0.005) according to the Anderson-Darling normality test. Non-parametric Spearman analysis showed a correlation between mean seed fresh weight per berry (SFW) and mean berry weight (BW) of 69.0%, 67.8% and 64.6% for the 2007, 2009 and 2010 seasons, respectively (α = 0.05). However, variations in BW values were explained by a weak linear relationship with SFW (r2 = 0.41, 0.43 and 0.46; P-value < 0.0001; F-value = 77.17, 98.35 and 106.70 for the 2007, 2009 and 2010 seasons, respectively Additional file 2).
Most of the heterozygous genotypes of the population, defined as such by the SSR VMC7F2 marker tightly linked to the SDI locus, were seedless and showed an average SFW below the population average, like (for instance) both heterozygous parental genotypes. The calculated dominance effect d was negative, showing that the seedless allele presents incomplete dominance (partial dominance) over the seeded allele. This partial dominance effect was also detected for berry weight, but the effect was lower. Finally, several offspring exhibited extreme phenotypes relative to the parents for both traits (Additional file 1). This phenotypic distribution was consistent with the heterozygosity in both parental genotypes of the SDI locus and the partial dominance of the seedless allele.
Construction of linkage group 18
Seedlessness and berry weight QTL analysis
QTLs identified for seed fresh weight (SFW) and berry weight (BW) on the consensus linkage group 18
Closest Marker to LOD peak
Var. Expl. MQM (%)
Marker Highest K-W
Var. Expl. K-W (%)
Mean (g) class: aa
Mean (g) class: ab
Mean (g) class. bb
Without intragenic markers for VvAGL11
With intragenic markers for VvAGL11
Parametric QTL analyses (IM and MQM) did not reveal significant differences between the parental genotypes in any of the evaluated seasons (2007, 2009, and 2010) for either of the two analyzed traits (not shown). Co-localizing QTLs were detected for SFW and BW, both centered on the VMC7F2 marker that was used as a cofactor for MQM analysis (Figure 1B and 1C). These QTLs explained most of the phenotypic variation in SFW (67.1%, 61.5% and 71.2% for the 2007, 2009 and 2010 seasons, respectively), and a minor proportion of the phenotypic variation in BW (33.0%, 33.9% and 36.9% for the same seasons, respectively; Table 1). Non-parametric analysis performed with the same marker used as a cofactor in the MQM analysis (VMC7F2) gave the highest Kruskal-Wallis values for SFW (75.7, 67.7 and 78.8 for the 2007, 2009 and 2010 seasons, respectively) and BW (38.5, 40.1 and 42.5 for the same seasons). Other minor QTLs were found on other linkage groups. However, none of them were consistent across seasons or in previous analyses performed in the same or other progeny [4, 9, 10, 15, 16]. Therefore, these other minor QTLs were not further assessed in the present work.
Positional candidate gene identification for SFW and BW
Lacking evidence that any of the remaining three annotated genes from this region could be involved in seed or berry development (Additional file 5), we decided to concentrate our further analysis on VvAGL11. Indeed, in grapevine, VvAGL11 has been shown to have carpel-specific RNA expression and to be highly expressed in flowers after the cap has been shed and in seeds [20, 21]. All these results and current knowledge of the possible functions of the genes in the region confirmed the former hypothesis of Costantini et al.  that VvAGL11 is the best positional candidate gene for the control of seed development. To obtain more evidence for a possible role of VvAGL11 in seedless table grapes, this positional candidate gene was characterized at the molecular, genetic and transcriptional levels.
Molecular characterization of VvAGL11alleles
Genotype, phenotype and relative expression of VvAGL11 of stable seedless or seeded individuals
Emperor × (Muscat of Alexandria × Sultanina)
RS × S
RS × S
RS × S
RS × S
(Hunisia × Emperor) × ((Hunisia × Emperor) × Nocera)
Normalized transcript abundance
Sequence polymorphisms in the promoter region and in putative regulatory elements
In the reference genome PN40024 , VvAGL11's putative regulatory region is defined as ~1,600 bp upstream of the TATA box and by a 5'UTR disrupted by an intron of ~ 1,200 bp (Figure 2B). Flanked by the same 5' and 3' ends, the seeded and seedless regulatory regions are 2,794 and 2,823 bp long, respectively. PN40024 and the seeded allele share 99.7% identity. By contrast, the seeded and seedless regulatory sequences have 96.8% identity with 13 INDELs and 22 SNPs differentiating the two alleles.
47 out of 118 cis-regulatory elements identified by PLACE  vary in number and position (Additional files 7 and 8). Among them several (GAGA)n cis-regulatory elements were identified as polymorphic in the putative regulatory region of VvAGL11 upstream and downstream from the transcription start site. In the Cauliflower Mosaic Virus 35S gene, GA-rich motifs positively affect promoter activity even when translocated upstream of the transcription start site , and in Arabidopsis, the first intron of AGL11 contains GA-rich motifs required for ovule- and septum-specific expression . Thus, the putative cis-regulatory elements identified in the 5'UTR intron of VvAGL11 might be functional. The SSR markers VMC7F2 (consistently reported as the closest marker to the SDI locus [4, 9, 15, 16]) and VvP18B20 (reported in this work) are located 420 and 350 bp, respectively, upstream of the TATA-box of the VvAGL11 gene, and the polymorphisms revealed by these SSR are (GAGA)n repeats (Additional file 8).
Sequence polymorphisms in the coding sequence
Genetic characterization of VvAGL11alleles
To acquire more precise information about a possible role of the coding and/or putative regulatory region of VvAGL11 in the seeded versus seedless phenotype, intragenic markers derived from allele sequencing were designed to perform a QTL analysis. Markers p1, p2 and p3_VvAGL11 were designed to genetically analyze INDELs in the regulatory region (Figure 2B and Additional file 3). An INDEL revealed by p1_VvAGL11 affects a putative O2-like box, p2_VvAGL11 marks a putative TATA-box near far the transcription start site and p3_VvAGL11 marks a (GAGA)n motif. Finally, marker e7_VvAGL11 was designed to test SNPs identified in exon 7 (Figure 2B, Additional file 7 and Additional file 3).
Transcriptional characterization of VvAGL11alleles
Validation of intragenic VvAGL11 markers in different genetic backgrounds
To extend the genetic analyses performed in the experimental progeny (RS × S) to different genetic backgrounds, an association analysis was performed with a population of 146 genotypes characterized quantitatively for seed fresh weight. The population, derived mainly from crosses of ten seedless varieties, revealed p3_VvAGL11 as the marker that explains the largest proportion of phenotypic variation. For markers VvP18B19, VMC7F2, p1, p2, p3_VvAGL111 and VvP18B32, the statistic Kruskal-Wallis values were 53.3, 56.0, 60.4, 63.8, 66.3 and 52.1 (P < 0.0001), respectively.
The p3_VvAGL11 marker revealed six different alleles (176, 188, 190, 192, 196 and 198 bp) and seven main genotypes (four additional at very low frequency). Most of the genotypes harboring one or two copies of the 198-bp allele have a seedless phenotype (Additional file 12). As described for the experimental progeny (198 and 188 bp alleles), the seedless allele (198 bp) has partial dominance over the 188 and 192 bp seeded alleles; however, the same effect was not detected with respect to the 176 bp seeded allele. Interestingly, all of the genotyped seedless varieties within this analysis were heterozygous for this locus (not shown).
Genetic dissection of seedlessness
Major QTLs for seed and berry weight were previously detected on LG18 in a subset of this progeny , in progeny derived from two other partially seedless genotypes  and in progeny derived from a cross of seeded and seedless genotypes . For SFW, confidence intervals varied between 6 and 12 cM in Doligez et al. , 6 and 8 cM in Cabezas et al.  and 20 cM in Mejía et al. . In the present work, integration of all the available genomic resources allowed us to quickly develop new co-dominant markers in the targeted area and to further reduce the confidence interval for this trait down to 1.5 cM with a segregating population of only ~ 125 phenotyped individuals. As the development of a well-balanced population in terms of phenotypic classes for seedlessness requires a step of in vitro embryo rescue , any strategy aiming to increase the accuracy of QTL detection without increasing the population size is of great interest. Moreover, genetic mapping of intragenic VvAGL11 markers, in addition to revealing a putative functional role of the regulatory of the coding region of VvAGL11, resulted in a narrower confidence interval (0.6 cM) for the SFW QTL, so far the narrowest QTL identified for this trait.
According to the genetic size of the most comprehensive SSR-based map for Vitis vinifera L.  and to the genome size reported for the grapevine genome , a confidence interval of 1.5 cM should be equivalent to ~ 500 kb. In our study, the confidence interval is equivalent to ~92 kb, indicating that this region may be hot spot for recombination, which allowed the mapping of intragenic VvAGL11 markers in a small progeny set (Additional file 13). However, genotyping errors in data sets are the most common source of variation and inflated genetic distances [44, 45]. For instance, intragenic variation could be due to replication slippage , the mutation mechanism that cause the hypervariability of microsatellites ([47, 48] cited in ). The putative regulatory region of VvAGL11 contains at least nine intragenic microsatellites annotated as (GAGA)n boxes (Not shown) with repeat units that vary from 4 to 13. Two genotypes of the RS × S experimental progeny presented a mutation, identified by SSR genotyping and sequence-verified, in the region amplified by marker p3_VvAGL11 (data not shown). This mutation consists of one additional unit of the GA repeat, which could have arisen either by Taq polymerase slippage during PCR or by a real mutation occurring in these genotypes. The use of a proofreading polymerase for the amplification and sequencing supports the latter hypothesis (data not shown). The limited size of our experimental population is also a potential source of distortions in genetic distance and QTL effect estimations. It is now well known that in such small populations, major effect QTLs are detected properly, but mapping experiments should be refined with larger populations and/or experimental designs adapted for the detection of environmental effects and minor QTLs [50, 51]. Indeed, the minor QTLs for BW and SFW detected in the present study, detected earlier in the same experimental population  and detected in other studies are neither coincident with each other nor stable among years [4, 9, 10, 15]. The positive correlation of seed and berry weights and the co-localization of major QTLs for both traits observed in this study was also detected and described in other progeny sets [9, 10, 15]. As already discussed in these former papers, this correlation could be due to (i) one underlying gene having a direct effect over ovule and seed development and indirectly affecting berry development through growth regulators produced by the developing seed, (ii) one or several genes having different and independent impacts on seed and berry development, or (iii) to a combination of both alternatives. The argument in favor of a pleiotropic effect of one gene is based on the fact that the growth of fleshy fruits mainly relies on cell division at early stages of berry development but on cell expansion after véraison . Cell division and expansion are both controlled by gibberellins, cytokinins and auxins, which are imported from seeds or ovules . However, because the partial dominance effect observed for seedlessness was less pronounced with respect to berry weight, it is probable that the same or other underlying genes have an independent influence on berry development.
Molecular dissection of the major QTL for seedlessness
The reduced confidence interval for the major seedlessness QTL corresponds to a 92 kb region of the grapevine genome sequence that contains four gene models. One of these corresponds to an ortholog of the MADS-box gene AGL11 in Arabidopsis thaliana  and FBP11 in Petunia hybrida , which were shown to be involved in the control of ovule identity. Based on current knowledge, none of the other genes are candidates for seed or berry development. Moreover, earlier expression studies in grapevine  suggested that VvAGL11 might influence ovule and seed development and that an alteration in this gene could yield seedless grapes. The expression profile described in this work for the seeded allele of VvAGL11 is consistent with what had been already reported in Syrah  and in Tempranillo , but also with the expression of orthologous genes like AGL11 [34, 39, 54], FBP11 [35, 55], TAGL11  and OsMADS13 . In Arabidopsis, Pynyopich et al.  showed that AGL11 is strongly expressed in the funiculus starting from the initial stages of ovule development, in mature ovules and after fertilization. They also showed that in agl11 mutants, seeds are rounder and smaller than in the wild type, and that funicular cells are greater in number and size, indicating that AGL11 is also required to prevent abnormal growth of the funiculus. Among the MADS-box genes known to control ovule identity [33, 54], AGL11 is the only one that seems to be both necessary and sufficient to promote ovule development . The others have proven to be redundant, suggesting that some of them evolved from a common ancestral gene . In Arabidopsis, the ectopic expression of STK (AGL11) promotes carpel development , and in grapes VvAGL11 is highly expressed in carpels  which ultimately develop into fruit, supporting the hypothesis that VvAGL11 might have a direct influence on berry development instead of merely a pleiotropic effect through seed development.
Alignment of the VvAGL11 and AGL11 nucleic and protein sequences showed that although the two proteins share 75% amino acid identity, no significant similarity exists between their promoter sequences. However, both predicted promoters are similar in length (~ 2.8 kb) and share 68% (93 of 136) of their cis-regulatory elements according to a signal scan performed with the PLACE database  over the AGL11 and VvAGL11 (not shown) regulatory regions. Also, the MM algorithm  (MEME method) identified the following shared motifs: [TC][CT][TC]T[CT]T[CT]T[TC]TC[TC][TC][TAC][CT]T[CT]T[CT]T[CT], with 19 and 17 motifs in Vitis (Vv) and Arabidopsis (At), respectively; G[AG]C[AC][AT][GC][AC]A[CT][CG][CA]A[CG], with 7 (Vv) and 2 (At); and C[AT]CAT[CT]TC[TC][CA][AC], with 9 (Vv) and 3 (At). The first (and more abundant) motif corresponds to (GAGA)n putative regulatory elements, which are the binding site for BASIC PENTACYSTEINE1 (BPC1), a regulator of the homeotic Arabidopsis thaliana gene AGL11, which controls ovule identity . BPC1 induces conformational changes by cooperative binding to purine-rich elements (GAGAn) present in the AGL11 regulatory sequence . Interestingly, these purine-rich repeats are abundant in the putative regulatory region of VvAGL11: at least six (GAGA)n were identified upstream of the TATA-box and three in the 5'UTR intron. The closest (GAGA)n repeats to the TATA-box correspond to three SSR markers segregating in the RS × S progeny, VMC7F2, VvB18B20 and p3_VvAGL11 (Figure 2 and Additional file 7). In this experimental population, p3_VvAGL11 and VMC7F2 explain up to 78% of the phenotypic variation in seedless, which make them very good candidates for being the main regulatory elements involved in the expression of the final seedless phenotype. In a selection of 146 genotypes derived from crosses of seedless × seedless varieties within our breeding program, p3_VvAGL11 yielded the highest Kruskall-Wallis value (up to 66%).
The proportion of phenotypic variation in seedlessness explained by VvAGL11 is huge, much greater than the estimated effect of other genes identified as QTLs from cultivated plants, like (for instance) ovate, which controls fruit shape in tomato (67%), and Se1, which controls flowering time in rice (67%) [59, 60]. As discussed above, even though this result is quite consistent with previous results using similar experimental designs [4, 9, 10, 15, 18], it must be taken carefully as some degree of distortion and/or overestimation of effects could exist due to the small size of our (and other) progeny sets, genotyping and phenotyping errors and recombination slippage events in the regulatory region of VvAGL11. Further analyses should be performed with larger experimental designs or by transgenic assays manipulating gene expression.
The mutations identified in regulatory elements of the seedless allele of VvAGL11 explained a slightly higher degree of phenotypic variation than those identified in the coding region (up to 13%, 6% and 13% more in 2007, 2009 and 2010, respectively), suggesting that the seedless phenotype might be genetically controlled by this regulatory region. Transcriptional analyses performed in contrasting phenotypes as well as in homozygous seeded and seedless genotypes revealed that in seedless genotypes, the expression of VvAGL11 was abolished during the period of rapid seed and berry growth after berry set. As expected, in heterozygous genotypes like Sultanina or Ruby Seedless, its expression was half that observed in homozygous seeded genotypes. Together, the genetic and transcriptional evidence suggest that seedlessness in table grapes might be due to misexpression of VvAGL11 caused by INDELs in its regulatory elements.
Defined by intragenic marker p3_VvAGL11, the seedless allele (198 bp) exerts a partially dominant effect over the seeded alleles (188 and 192 bp): most of the heterozygous genotypes are seedless. The C-domain in the coding sequence has been described as the less conserved domain between the MADS-box family members . However, each of the major MIKC sub-families possesses short, highly conserved motifs [61, 62] whose specific function remains unknown . The C-domain has also been reported to be involved in the mediation of higher-order interactions among MADS protein dimers [42, 64], in transcriptional activation [42, 65], and in post-translational modifications . A non-silent mutation identified in one of these conserved motifs of VvAGL11 that did not by itself explain the seedless phenotype might be responsible for a structural change in the C-domain making the mutant transcription factor barely expressed during the initial stages of seed development and therefore dominant over its wild-type alleles.
Altogether, these results are partially agree with the model proposed by Bouquet and Danglot  and Lahogue et al.  for the control of seed development, where a single dominant locus, SDI, codes for a major regulatory gene. The three remaining loci that interact with SDI, according to the proposed model, were not identified with the current experimental design.
In a perennial species such as grapevine, markers that allow individuals not carrying the favorable allele for the most desirable given trait to be discarded before planting in the field for further evaluation are invaluable. This is especially true for phenotypes that can only be screened in adult plants such as those affecting berries.
By identifying several interesting intragenic polymorphisms between seeded and seedless genotypes in the VvAGL11 regulatory region (p1_VvAGL11, p2_VvAGL11 and p3_VvAGL11), our study provides four new intragenic markers in a candidate gene for seedlessness for breeding purposes. These intragenic markers displayed different relative efficiencies measured as the phenotypic variation explained by the marker and based on their efficiency to select positively seedless genotypes or negatively seeded genotypes. The SSR marker VMC7F2, already described as the closest marker to the SDI locus [4, 9, 16], was confirmed as one of the best markers for progeny screening. Association analysis performed in the RS × S experimental progeny and over a population derived from several other seedless × seedless crosses revealed p3_VvAGL11 as the most reliable marker for breeding purposes over three different seasons and across different genetic backgrounds.
The two most interesting markers identified in our work or former studies (p3_VvAGL11 and VMC7F2) need to be tested for their robustness in larger genetic backgrounds segregating for seedlessness. Lahogue et al.  developed the SCAR marker SCC8, which is tightly linked to the SDI locus; however, SCC8 was not useful in all the evaluated progeny [13, 18] or in the RS × S experimental population (not shown), as it often amplifies a null allele . In a controlled population derived from Dominga × Autumn Seedless, Cabezas et al.  identified SSR markers closely linked to the SDI locus (VMC7F2 and VMC6F11), and these markers results in 4% to 6% false positive identifications (seeded hybrids identified as seedless) and in 11% to 13% false negatives. In the experimental population analysed in the present work, the use of marker p3_VvAGL11 for the selection of homozygous genotypes resulted in 0% false positives (Figure 4), while VMC2F2 yielded 5% false positives (data not shown). Haplotype analysis, defined either by combinations marker pairs or by all the intragenic markers for VvAGL11 (p1, p2, p3_VvAGL11 and VMC7F2) failed to improve the efficiency achieved by p3_VvAGL11 alone in our experimental population; any combination not only gave the same number of selected true seedless phenotypes but also increased the number of seeded phenotypes identified by mistake as true seedless (not shown).
VvAGL11 belongs to the D-lineage of MADS-box genes that control ovule identity. A better understanding of its function would benefit other crops, as its function seems to be conserved across the plant species already studied (A. thaliana, Petunia...). However, its function in grapevine remains to be proven by genetic transformation of seeded cultivars. Whether its role in seedlessness is confirmed or not, VvAGL11 has proven to be a very useful marker for assisted selection of seedless grapevine.
For QTL mapping experiments, full sib progeny were obtained via embryo rescue  from a cross between Ruby Seedless and Sultanina (RS × S ; N = 139); seedlings from this progeny were grown on their own roots or over Sultanina rootstock as a replicate. For validation purposes, 146 mature seedlings derived from 14 different crosses between 11 seedless varieties were used for genotyping and phenotyping experiments. All genotypes were grown at La Platina Experimental Station of the Instituto de Investigaciones Agropecuarias, Santiago, Chile. A core collection (N = 21) was also used to test the association between the identified polymorphisms and traits, and this collection contains a representative sample of diversity in cultivated Vitis vinifera L. and in different Vitis species and genera  (Additional file 10). The core collection and genotypes of the Vitis genus are held by INRA Montpellier, France, at the domain of Vassal, F-34340 Marseillan (http://www.montpellier.inra.fr/vassal). The core collection is a sub-sample of 48 varieties selected based on their genotypes for 20 SSR markers using the M-strategy. This core collection, highly non-redundant and highly diverse, represents 83% of the total SSR diversity  from the world largest germplasm collection of cultivated Vitis vinifera, 3,900 accessions corresponding to 2,262 unique genotypes (Laucou et al. cited in ). In all cases, genomic DNA was extracted according to Lodhi et al.  from 100 mg of young immature leaves (not fully expanded) collected two weeks after bud-break and kept at -80°C or lyophilized until DNA extraction.
Seedlessness can be dissected into three main sub-traits, seed fresh weight, seed dry weight and seed number [9, 10]. In this work, seedlessness was analysed as seed fresh weight because no significant differences were found between fresh and dry weight in a preliminary analysis  and because seed number analysis is subject to bias due to the subjectivity of determining and differentiating true seeds from large rudiments, or rudiment traces from ovule traces.
Phenotypic data were recorded using an improved protocol from 115, 126 and 122 mature individuals from the 2007, 2009 and 2010 seasons, respectively, which are 17, 28 and 24 more than in the former QTL detection study with the same progeny . Briefly, both berry weight (g) (BW) and seed fresh weight (g) (SFW) were scored at the ripening stage (17 ° Brix). For BW and SFW, 300 berries and seeds from 150 berries, respectively, were randomly sampled and weighed in three different clusters of each genotype. Quantitative analyses were performed of the mean BW per genotype and the mean SFW per berry and per genotype. For validation purposes, the same phenotyping strategy was used to analyze a population (n = 146) issued from 14 different crosses between common seedless varieties: Sultanina × Ruby Seedless (n = 30), Beauty Seedless × Crimson Seedless (n = 19), Red Seedless × Flame Seedless (n = 5), Ruby Seedless × Perlette (n = 7), Sultanina × Black Seedless (n = 9), Flame × Black Seedless (n = 10), Ruby Seedless × Superior Seedless (n = 9), Ruby Seedless × Dawn Seedless (n = 28), Flame Seedless × Perlette (n = 3), Flame Seedless × Beauty Seedless (n = 4), Ruby Seedless × Beauty Seedless (n = 4), Red Seedless × Dawn Seedless (n = 7), Sultanina × Dawn Seedless (n = 7) and Sultanina × Superior Seedless (n = 4). Association analysis was performed by one-way ANOVA, significative differences were tested at P < 0.05 by Fisher's least significant difference procedure.
The dominance effect d was calculated according to Acquaah  as follows: d = Mab -[(Maa + Mbb)/2] where M is the phenotypic mean of the genotypes (aa seedless homozygous genotypes, bb seeded homozygous genotypes and ab heterozygous genotypes); if d < 0, the a allele presents incomplete dominance (partial dominance) over the b allele.
SSR and VvAGL11genotyping
To reduce the confidence interval of the major seedlessness QTL identified previously on chromosome 18, a total of 13 publicly available SSR primer pairs were selected according to the Costantini et al.  strategy and based on existing reference maps [43, 71, 72]. Fifteen new SSR markers were developed from Cabernet-Sauvignon BAC End Sequences (BES)  or from the currently available assemblies of the grapevine genome sequencing project [1, 32] using the SSRIT software ; the developed SSR markers are described in Additional file 3. The SSR search was directed to the QTL-containing region or to poorly integrated regions between the physical and genetic maps. As an example, in the region of the SSR marker VMC7F2, both BES of the BAC contig n°1821 of the Cabernet-Sauvignon physical map (http://urgi.versailles.inra.fr/cmap) and sequences from the 6X genome assembly [1, 32] matching these BES were used, comparisons between BES and sequences from the genome assembly were performed by BLASTn . Primers were designed using the Primer3 software , and they were used for BAC anchoring experiments according to Lamoureux et al.  and for genetic mapping experiments.
VvAGL11 was identified as the most evident positional candidate gene in the defined confidence interval for the major seedlessness QTL on chromosome 18. As soon as the 8.4x annotated grapevine genome sequence was available [1, 32], its annotation was used to confirm its true orthologous relationship by a reciprocal best match procedure as described in . Gene models and predicted coding sequences from the automatic annotation of the grapevine genome sequence  that were identified in QTL regions were carefully checked using the available resources. In particular, we checked the alignment of Vitis ESTs from public databases (NCBI) or from a private EST database  that holds 18,366 ESTs derived from libraries of different floral and berry developmental stages in cvs. Sultanina and Carmenère.
General genotyping PCR amplifications were done in a 10-μL reaction mixture containing 0.25 μM each primer, 0.25 mM each dNTP, 1.6 mM MgCl2, 0.25 U Taq polymerase, 25 ng of template DNA, 0.2 mM Red Cresol and 12% sucrose. An Amp® PCR system 9700 (PE Applied Biosystems) was programmed as follows for PCR amplification: 30 sec at 95°C, annealing (30 sec at 58°C), and extension (30 sec at 72°C) for 35 cycles, followed by a fill-in step of 4 min at 72°C. SSRs were resolved by denaturing acrylamide gel electrophoresis according to Creste et al.  with some modifications: a 6% acrylamide solution 37.5:1 (acrylamide:bisacrylamide) with 7 M urea and 3.75% glycerol was used. SSCPs were resolved in MDE (FMC BioProducts Inc) gels according to Martins-Lopes et al.  or in native 8% acrylamide (37.5:1) and 5% glycerol gels. After electrophoresis in native, denaturing or MDE gels, the amplified fragments were revealed by silver staining according to Creste et al. . For VvAGL11 intragenic markers (Additional file 3) the annealing temperature was set to 64°C, the rest was as above. For p3_VvAGL11 specifically, PCR products labeled with PET dye were resolved by capillary electrophoresis according to standard procedures recommended for the ABI 3130xl Genetic Analyzer; the other parameters used were as described above.
Genetic map construction for LG18
In heterozygous plant species like Vitis, the various marker pairs segregation type greatly differ in their accuracy for estimation of recombination frequency with regard to the power for detecting linkage . After markers have been assigned to linkage groups, conflicting information with respect to the marker order is often provided by the different pairwise recombination frequency estimates. This can be due to missing marker data, but also to random estimation errors in the recombination frequency inherent to the marker configurations . To reduce such problems, we built linkage group 18 using co-dominant markers only and the fixed order option based on the available genomic sequence [1, 32]. The double pseudo-testcross strategy  and JoinMap 3.0 software  were used to automatically determine the phases and to build the genetic map. Markers with high segregation distortion, unexpected χ2 test results or null alleles (a_ × ab; ab × a_) that cannot be handled by JoinMap 3.0 were discarded or scored as dominant markers. The LOD score and recombination threshold for the determination of linkage groups were, respectively set at 3.5 and 0.4. Markers within the resulting groups were ordered relative to each other by automatic multipoint analyses using the default values of JoinMap 3.0 (mapping threshold LOD > 1, REC < 0.4). Parental maps were constructed as two cross-pollinated populations. A consensus map was constructed using the parameters for a cross-pollinated derived population and the integrate map function of JoinMap 3.0. Recombination units were transformed into genetic distances using the Kosambi function . The linkage group was numbered according to the recommendation of the IGGP .
Phenotypic data were submitted to basic statistics and normality tests with Minitab 15 software (Minitab Inc). Data were normalized with the Johnson transformation included in Minitab 15. QTL detection and analyses by interval mapping  were performed separately for both parental and consensus framework maps using MapQTL 4.0  and the normalized data for BW and SFW. To establish the confidence of a putative QTL, the following strategy was undertaken. For each putative QTL, the closest markers to the peak of the LOD profile were tested using the Automatic Cofactor Selection procedure. Markers accepted as co-factors where then used to perform a Multiple QTL Mapping test and to determine the total phenotypic variation explained by these markers. In parallel, a Permutation Test (1,000 permutations, genome-wise and chromosome-wise type error rate of 0.05) was used to establish the threshold level at which a QTL was declared significant or suggestive . QTLs were established as significant when the detected LOD was higher than the threshold LOD for a genome-wise type error. One-LOD and two-LOD support confidence intervals were constructed for each QTL . Associations between alleles of intragenic VvAGL11 markers and phenotypes were further assessed with the non-parametric Kruskal-Wallis (KW) rank-sum test using the non-normalized phenotypic data.
VvAGL11 has an expected size near 10 kb comprising the putative regulatory and coding regions. Besides, it is in heterozygous state in both parental genotypes, which makes amplification, cloning and sequence assembly difficult. Therefore, we decided to isolate the regulatory sequence from DNA and the coding sequence from cDNA, both isolated in homozygous genotypes (defined by their genotype at the VMC7F2 marker).
Primers were designed with the Primer3Plus web interface  using the sequencing option and the PN40024 genome sequence as the template (Additional file 14). PCR products were amplified in the same conditions as described for the genotyping procedure, and the amplicons were purified with a QIAEX II® Gel Extraction Kit (QIAGEN) and cloned into pGEM-T-Easy® (Promega) for sequencing. Sequence trimming and contig assembly were performed with Geneious® . The partially sequenced regulatory region corresponds to ~1.5 kb upstream and ~1.4 kb downstream of the TATA box, and the 1.4 kb region includes the 5'UTR intron. Regulatory sequence analysis of VvAGL11 from PN40024 and from both Sultanina-derived alleles was performed using the PLACE database . The search for conserved motifs in the regulatory region between Vitis and Arabidopsis was performed by the MEME method .
The coding region was cloned and sequenced from RNA isolated from the same genotypes as described above in three different developmental stages (I, J and K according to Baggiolini ). Total RNA was extracted with the FavorPrep Total RNA Mini Kit for Woody Plants® (FAVORGEN), the mRNA was purified with Dynabeads® Oligo(dT) (INVITROGEN) and cDNA was amplified with SuperScript III RT® (INVITROGEN). The oligos for VvAGL11 CDS isolation are 5'-ATGGGGAGAGGAAAGATCGA-3' and 5'-TACCCGAGATGGAGGACCTT-3', and the PCR conditions were the same as described above. Bands of the expected size (671 bp) were cut from agarose gels and purified and cloned as described above; four clones from each genotype were sequenced.
Genetic analysis of VvAGL11polymorphisms
Four intragenic markers were developed located in the regulatory (3) and coding (1) regions: p1, p2 and p3_VvAGL11 and e7_VvAGL11, respectively (Additional file 3 and Figure 2B). The p1, p2 and p3 markers are SSR-like and e7 is an SSCP marker. e7_VvAGL11 amplicons from two representative seedlings of each genotype (four genotypes 1:1:1:1, ee, ef, eg, fg) plus both parental genotypes (ef and eg for RS and S, respectively) were cloned into pGEM-T-Easy® (Promega). Clones showing different inserts (alleles) were chosen by SSCP analysis for sequencing using transformed colonies directly as PCR templates. The region containing the marker p3_VvAGL11 and defined as the putative minimal promoter was amplified using template DNA from a seeded genotype that presented a new second allele using AccuPrime Pfx DNA polymerase (Invitrogen) and cloned into pENTR/D-TOPO (Invitrogen). The oligos used to isolate this region are 5'-caccTTGTGGCCTTGAAGAAA-3' and 5'-CACAATGGAGAGATGTGAGACG-3', and the manufacturer's conditions were followed for the PCR, purification and ligation reactions.
Real-time quantitative PCR (qPCR) assays
The transcript abundance of VvAGL11 was evaluated in the four genotypes of the RS × S progeny described above for sequence characterization: both heterozygous seedless parents of the progeny (Ruby Seedless and Sultanina), and an unrelated seeded common table grape genotype, Red Globe. Expression analysis was performed at three developmental stages of fruit development (pre-bloom, I; bloom, J; and fruit set with berries showing 5-10 mm equatorial diameter, K) according to Baggiolini ). Three biological samples where independently analyzed for each genotype × stage combination.
qPCR was performed with the LightCycler® (Roche Diagnostics) real-time PCR system using SYBR Green® as the fluorescent dye to measure DNA amplicons derived from mRNA. A 100-ng aliquot of mRNA was used as the template for reverse transcription reactions to synthesize single-stranded cDNA using the SuperScript III® system and oligo(dT) primers (INVITROGEN) according to standard procedures. Gene-specific primers were designed with Primer3  considering exon-exon junctions. For VvAGL11, the oligos are 5'-GCAGAAGTTGCCCTCATCGT-3' and 5'-AAGCCAAGGAATCACCCATT-3'; for the internal reference gene EF1-α (GSVIVT00024496001-8.4x) the oligos are 5'-AGGATGGACAAACCCGTGAG-3' and 5'-AAGCCAGAGATGGGGACAAA-3', and the amplicons have a predicted size of 232 bp and 202 bp, respectively. For each gene, a calibration curve was constructed by measuring the fluorescence of four serial dilutions (101-10-2 pg ul-1) of an RT-PCR product obtained with the same oligos and cDNA from PN40024 as the template to estimate copy numbers in total cDNA.
The amplification reaction was carried out in a total volume of 20 μl containing 1 pmol of each primer, 1.5 mM MgCl2, 1 μl of LightCycler® DNA Master SYBR Green I (containing 1.25 U of Taq polymerase, 10× Taq buffer (500 mM KCl, 100 mM TRIS-HCl, pH 8.3), dNTPs each at 2 mM, 10× SYBR Green I; (Roche Diagnostics) and 100 ng of cDNA prepared as described above.
The thermal conditions for qPCR were as follows: denaturation at 95°C for 10 min, followed by 35 three-step cycles of template denaturation at 95°C with a 2 s hold, primer annealing at 60°C for 10 s, and extension at 72°C for 20 s. Fluorescence data were collected after each extension step. Melting curve analyses were performed by heating the template at 95°C with a 0 s hold, then cooling to 60°C with a 15 s hold, and finally increasing the temperature to 95°C with a 0.1°C s-1 temperature transition rate while continuously monitoring the fluorescence. All other phases were performed with a 20°C s-1 transition rate. Fluorescence was analyzed using LightCycler® Analysis Software. The crossing point for each reaction was determined using the second derivative maximum algorithm and manual baseline adjustment. In all cases, the melting curves were checked for single peaks, and the amplification product sizes were confirmed in agarose gels to ensure the absence of non-specific PCR products. Duplicate qPCR experiments were performed for each sample. If a statistical difference was found between the two replicates, one to two additional replicates were added. The expression values were normalized against EF1-α. To test whether EF1-α behaved as a housekeeping gene in the analyzed samples, cDNA samples from the three stages of berry development (I, J and K) were analyzed comparing EF1-α and actin as a control transcript (GSVIVT00034893001, primers 5'-GCTGGATTCTGGTGATGGTG-3' and 5'-CCAATGAGAGATGGCTGGAA-3', 348 bp product size). For each cDNA, the transcript abundances of EF1-α and actin were analyzed by qPCR and the ratios of the control transcript to the endogenous EF1-α transcript were calculated. The results indicated that the abundance of EF1-α mRNA remained stable between samples (data not shown). qPCR data normalized with the LOG10 function and subjected to statistical analyses of variance and treatment means were separated using Tukey's Post-hoc test at P = 0.05 with Prism® v4.0 (GRAPHPAD).
The project benefited from a Marie Curie Host Fellowship for Early Stage Research Training (EU program) in the frame of the VERT project. Additionally, this project was financially supported by Biofrutales S.A - Programa Bicentenario de Ciencia y Tecnología - Conicyt, PBCT - Conicyt PSD-03 and, CORFO-INNOVA grant 08CT11PUD-07 and FONDEF G07I1002. The authors thank Marco Moroldo and Aurelie Canaguier for help and advice during the mapping experiments, and Mauricio González-Agüero for the real-time PCR analysis support. This work is dedicated to the memory of Ximena Casanueva.
- Jaillon O, Aury JM, Noel B, Policriti A, Clepet C, Casagrande A, Choisne N, Aubourg S, Vitulo N, Jubin C, et al: The grapevine genome sequence suggests ancestral hexaploidization in major angiosperm phyla. Nature. 2007, 449 (7161): 463-467. 10.1038/nature06148.PubMedView ArticleGoogle Scholar
- Velasco R, Zharkikh A, Troggio M, Cartwright DA, Cestaro A, Pruss D, Pindo M, Fitzgerald LM, Vezzulli S, Reid J, et al: A high quality draft consensus sequence of the genome of a heterozygous grapevine variety. PLoS ONE. 2007, 2 (12): e1326-10.1371/journal.pone.0001326.PubMedPubMed CentralView ArticleGoogle Scholar
- Carmona MJ, Chaib J, Martinez-Zapater JM, Thomas MR: A molecular genetic perspective of reproductive development in grapevine. J Exp Bot. 2008, 59 (10): 2579-2596. 10.1093/jxb/ern160.PubMedView ArticleGoogle Scholar
- Costantini L, Battilana J, Lamaj F, Fanizza G, Grando M: Berry and phenology-related traits in grapevine (Vitis vinifera L.): From Quantitative Trait Loci to underlying genes. BMC Plant Biology. 2008, 8 (1): 38-10.1186/1471-2229-8-38.PubMedPubMed CentralView ArticleGoogle Scholar
- Moroldo M: Physical mapping and sequencing of the genomes of grapevine (PhD. Thesis). Udine: University of Udine; 2006.Google Scholar
- Emanuelli F, Battilana J, Costantini L, Le Cunff L, Boursiquot JM, This P, Grando M: A candidate gene association study on muscat flavor in grapevine (Vitis vinifera L.). BMC Plant Biology. 2010, 10 (1): 241-10.1186/1471-2229-10-241.PubMedPubMed CentralView ArticleGoogle Scholar
- Ledbetter CA, Ramming DW: Seedlessness in grapes. Hort Rev. 1989, 11: 159-184.Google Scholar
- Ledbetter CA, Burgos L: Inheritance of stenospermocarpic seedlessness in Vitis vinifera L. J Hered. 1994, 85 (2): 157-160.Google Scholar
- Cabezas JA, Cervera MT, Ruiz-Garcia L, Carreno J, Martinez-Zapater JM: A genetic analysis of seed and berry weight in grapevine. Genome. 2006, 49 (12): 1572-1585. 10.1139/G06-122.PubMedView ArticleGoogle Scholar
- Doligez A, Bouquet A, Danglot Y, Lahogue F, Riaz S, Meredith P, Edwards J, This P: Genetic mapping of grapevine (Vitis vinifera L.) applied to the detection of QTLs for seedlessness and berry weight. Theor Appl Genet. 2002, 105 (5): 780-795. 10.1007/s00122-002-0951-z.PubMedView ArticleGoogle Scholar
- Striem MJ, Spiegel-Roy P, Baron I, Sahar N: The degrees of development of the seed-coat and endosperm as separate subtraits of stenospermocarpic seedlessness in grapes. Vitis. 1992, 31: 149-155.Google Scholar
- Cain DW, Emershad RL, Tarailo RE: In-ovulo embryo culture and seedling development of seeded and seedless grapes (Vitis vinifera L.). Vitis. 1983, 22 (1): 9-14.Google Scholar
- Adam-Blondon AF, Lahogue F, Bouquet A, Boursiquot JM, This P: Usefulness of two SCAR markers for marker-assisted selection of seedless grapevine cultivars. Vitis. 2001, 40 (3): 147-155.Google Scholar
- Bouquet A, Danglot Y: Inheritance of seedlessness in grapevine (Vitis vinifera L.). Vitis. 1996, 35: 35-42.Google Scholar
- Fanizza G, Lamaj F, Costantini L, Chaabane R, Grando MS: QTL analysis for fruit yield components in table grapes (Vitis vinifera). Theor Appl Genet. 2005, 111 (4): 658-664. 10.1007/s00122-005-2016-6.PubMedView ArticleGoogle Scholar
- Mejía N, Gebauer M, Muñoz L, Hewstone N, Muñoz C, Hinrichsen P: Identification of QTLs for seedlessness, berry size, and ripening date in a seedless × seedless progeny. Am J Enol Vitic. 2007, 58 (4): 499-507.Google Scholar
- Striem MJ, Ben-Hayyim G, Spiegel-Roy P: Identifying molecular genetic markers associated with seedlessness in grape. J Amer Soc Hort Sci. 1996, 121 (5): 758-763.Google Scholar
- Lahogue F, This P, Bouquet A: Identification of a codominant scar marker linked to the seedlessness character in grapevine. Theor Appl Genet. 1998, 97: 950-959. 10.1007/s001220050976.View ArticleGoogle Scholar
- Immink RG, Nougalli Tonaco IA, de Folter S, Shchennikova A, van Dijk AD, Busscher-Lange J, Borst JW, Angenent GC: SEPALLATA3: The "glue" for MADS box transcription factor complex formation. Genome Biol. 2009, 10 (2): R24-10.1186/gb-2009-10-2-r24.PubMedPubMed CentralView ArticleGoogle Scholar
- Diaz-Riquelme J, Lijavetzky D, Martinez-Zapater JM, Carmona MJ: Genome-Wide Analysis of MIKCC-Type MADS-Box Genes in Grapevine. Plant Physiol. 2008Google Scholar
- Boss PK, Sensi E, Hua C, Davies C, Thomas MR: Cloning and characterization of grapevine (Vitis vinifera L.) MADS-box genes expressed during inflorescence and berry development. Plant Sci. 2002, 162: 887-895. 10.1016/S0168-9452(02)00034-1.View ArticleGoogle Scholar
- Hanania U, Velcheva M, Or E, Flaishman M, Sahar N, Perl A: Silencing of chaperonin 21, that was differentially expressed in inflorescence of seedless and seeded grapes, promoted seed abortion in tobacco and tomato fruits. Transgenic Res. 2007, 16 (4): 515-525. 10.1007/s11248-006-9044-0.PubMedView ArticleGoogle Scholar
- Hanania U, Velcheva M, Sahar N, Flaishman M, Or E, Degani O, Perl A: The ubiquitin extension protein S27a is differentially expressed in developing flower organs of Thompson seedless versus Thompson seeded grape isogenic clones. Plant Cell Rep. 2009, 28 (7): 1033-1042. 10.1007/s00299-009-0715-1.PubMedView ArticleGoogle Scholar
- Coombe BG: The development of fleshy fruits. Ann Rev Plant Physiol. 1976, 27: 507-528. 10.1146/annurev.pp.27.060176.001231.View ArticleGoogle Scholar
- Ollat N, Diakou-Verdin P, Carde JP, Barrieu F, G JP, Moing A: Grape berry development: A review. Journal International des Sciences de la Vigne et du Vin. 2002, 36 (3): 109-131.Google Scholar
- Deluc LG, Grimplet J, Wheatley MD, Tillett RL, Quilici DR, Osborne C, Schooley DA, Schlauch KA, Cushman JC, Cramer GR: Transcriptomic and metabolite analyses of Cabernet Sauvignon grape berry development. BMC Genomics. 2007, 8 (1): 429-10.1186/1471-2164-8-429.PubMedPubMed CentralView ArticleGoogle Scholar
- Pilati S, Perazzolli M, Malossini A, Cestaro A, Dematte L, Fontana P, Dal Ri A, Viola R, Velasco R, Moser C: Genome-wide transcriptional analysis of grapevine berry ripening reveals a set of genes similarly modulated during three seasons and the occurrence of an oxidative burst at veraison. BMC Genomics. 2007, 8 (1): 428-10.1186/1471-2164-8-428.PubMedPubMed CentralView ArticleGoogle Scholar
- Davies C, Robinson SP: Differential screening indicates a dramatic change in mRNA profiles during grape berry ripening. Cloning and characterization of cDNAs encoding putative cell wall and stress response proteins. Plant Physiol. 2000, 122: 803-812. 10.1104/pp.122.3.803.PubMedPubMed CentralView ArticleGoogle Scholar
- Goes da Silva F, Iandolino A, Al-Kayal F, Bohlmann MC, Cushman MA, Lim H, Ergul A, Figueroa R, Kabuloglu EK, Osborne C, et al: Characterizing the grape transcriptome. Analysis of expressed sequence tags from multiple Vitis species and development of a compendium of gene expression during berry development. Plant Physiol. 2005, 139 (2): 574-597. 10.1104/pp.105.065748.View ArticleGoogle Scholar
- Peng FY, Reid KE, Liao N, Schlosser J, Lijavetzky D, Holt R, Martinez Zapater JM, Jones S, Marra M, Bohlmann J, et al: Generation of ESTs in Vitis vinifera wine grape (Cabernet Sauvignon) and table grape (Muscat Hamburg) and discovery of new candidate genes with potential roles in berry development. Gene. 2007, 402 (1-2): 40-50. 10.1016/j.gene.2007.07.016.PubMedView ArticleGoogle Scholar
- Terrier N, Glissant D, Grimplet J, Barrieu F, Abbal P, Couture C, Ageorges A, Atanassova R, Léon C, Renaudin JP, et al: Isogene specific oligo arrays reveal multifaceted changes in gene expression during grape berry (Vitis vinifera L.) development. Planta. 2005, 222: 832-847. 10.1007/s00425-005-0017-y.PubMedView ArticleGoogle Scholar
- Centre National de Séquençage. http://www.genoscope.cns.fr/vitis, [http://www.genoscope.cns.fr/externe/GenomeBrowser/Vitis/]
- Favaro R, Pinyopich A, Battaglia R, Kooiker M, Borghi L, Ditta G, Yanofsky MF, Kater MM, Colombo L: MADS-box protein complexes control carpel and ovule development in Arabidopsis. Plant Cell. 2003, 15 (11): 2603-2611. 10.1105/tpc.015123.PubMedPubMed CentralView ArticleGoogle Scholar
- Rounsley S, Ditta G, Yanofsky M: Diverse roles for MADS box genes in Arabidopsis development. Plant Cell. 1995, 7 (8): 1259-1269. 10.1105/tpc.7.8.1259.PubMedPubMed CentralView ArticleGoogle Scholar
- Colombo L, Franken J, Koetje E, van Went J, Dons H, Angenent G, van Tunen A: The petunia MADS box gene FBP11 determines ovule identity. Plant Cell. 1995, 7 (11): 1859-1868. 10.1105/tpc.7.11.1859.PubMedPubMed CentralView ArticleGoogle Scholar
- Kramer EM, Jaramillo MA, Di Stilio VS: Patterns of gene duplication and functional evolution during the diversification of the AGAMOUS subfamily of MADS box genes in angiosperms. Genetics. 2004, 166 (2): 1011-1023. 10.1534/genetics.166.2.1011.PubMedPubMed CentralView ArticleGoogle Scholar
- Higo K, Ugawa Y, Iwamoto M, Korenaga T: Plant cis-acting regulatory DNA elements (PLACE) database: 1999. Nucleic Acids Res. 1999, 27 (1): 297-300. 10.1093/nar/27.1.297.PubMedPubMed CentralView ArticleGoogle Scholar
- Pauli S, Rothnie HM, Chen G, He X, Hohn T: The cauliflower mosaic virus 35S promoter extends into the transcribed region. J Virol. 2004, 78 (22): 12120-12128. 10.1128/JVI.78.22.12120-12128.2004.PubMedPubMed CentralView ArticleGoogle Scholar
- Kooiker M, Airoldi CA, Losa A, Manzotti PS, Finzi L, Kater MM, Colombo L: BASIC PENTACYSTEINE1, a GA binding protein that induces conformational changes in the regulatory region of the homeotic Arabidopsis gene SEEDSTICK. Plant Cell. 2005, 17 (3): 722-729. 10.1105/tpc.104.030130.PubMedPubMed CentralView ArticleGoogle Scholar
- Parenicova L, de Folter S, Kieffer M, Horner DS, Favalli C, Busscher J, Cook HE, Ingram RM, Kater MM, Davies B, et al: Molecular and phylogenetic analyses of the complete MADS-box transcription factor family in Arabidopsis: new openings to the MADS world. Plant Cell. 2003, 15 (7): 1538-1551. 10.1105/tpc.011544.PubMedPubMed CentralView ArticleGoogle Scholar
- Egea-Cortines M, Saedler H, Sommer H: Ternary complex formation between the MADS-box proteins SQUAMOSA, DEFICIENS and GLOBOSA is involved in the control of floral architecture in Antirrhinum majus. EMBO J. 1999, 18 (19): 5370-5379. 10.1093/emboj/18.19.5370.PubMedPubMed CentralView ArticleGoogle Scholar
- Honma T, Goto K: Complexes of MADS-box proteins are sufficient to convert leaves into floral organs. Nature. 2001, 409 (6819): 525-529. 10.1038/35054083.PubMedView ArticleGoogle Scholar
- Doligez A, Adam-Blondon AF, Cipriani G, Di Gaspero G, Laucou V, Merdinoglu D, Meredith CP, Riaz S, Roux C, This P: An integrated SSR map of grapevine based on five mapping populations. Theor Appl Genet. 2006, 113 (3): 369-382. 10.1007/s00122-006-0295-1.PubMedView ArticleGoogle Scholar
- Cartwright DA, Troggio M, Velasco R, Gutin A: Genetic mapping in the presence of genotyping errors. Genetics. 2007, 176 (4): 2521-2527. 10.1534/genetics.106.063982.PubMedPubMed CentralView ArticleGoogle Scholar
- Pompanon F, Bonin A, Bellemain E, Taberlet P: Genotyping errors: causes, consequences and solutions. Nat Rev Genet. 2005, 6 (11): 847-859. 10.1038/nrg1707.PubMedView ArticleGoogle Scholar
- Tautz D, Schlotterer : Simple sequences. Curr Opin Genet Dev. 1994, 4 (6): 832-837. 10.1016/0959-437X(94)90067-1.PubMedView ArticleGoogle Scholar
- Lai Y, Shinde D, Arnheim N, Sun F: The mutation process of microsatellites during the polymerase chain reaction. J Comput Biol. 2003, 10 (2): 143-155. 10.1089/106652703321825937.PubMedView ArticleGoogle Scholar
- Shinde D, Lai Y, Sun F, Arnheim N: Taq DNA polymerase slippage mutation rates measured by PCR and quasi-likelihood analysis: (CA/GT)n and (A/T)n microsatellites. Nucleic Acids Res. 2003, 31 (3): 974-980. 10.1093/nar/gkg178.PubMedPubMed CentralView ArticleGoogle Scholar
- Ellegren H: Microsatellites: simple sequences with complex evolution. Nat Rev Genet. 2004, 5 (6): 435-445. 10.1038/nrg1348.PubMedView ArticleGoogle Scholar
- da Costa e Silva L, Damiao Cruz C, Alves Moreira M, Goncalvez de Barros E: Simulation of population size and genome saturation level for genetic mapping of recombinant inbred lines. Genet Mol Biol. 2007, 30 (4): 1101-1108.View ArticleGoogle Scholar
- Vales MI, Schon CC, Capettini F, Chen XM, Corey AE, Mather DE, Mundt CC, Richardson KL, Sandoval-Islas JS, Utz HF, et al: Effect of population size on the estimation of QTL: a test using resistance to barley stripe rust. Theor Appl Genet. 2005, 111 (7): 1260-1270. 10.1007/s00122-005-0043-y.PubMedView ArticleGoogle Scholar
- Cheniclet C, Rong WY, Causse M, Frangne N, Bolling L, Carde JP, Renaudin JP: Cell expansion and endoreduplication show a large genetic variability in pericarp and contribute strongly to tomato fruit growth. Plant Physiol. 2005, 139 (4): 1984-1994. 10.1104/pp.105.068767.PubMedPubMed CentralView ArticleGoogle Scholar
- Blouin J, Guimberteau G: Maturation et maturité des raisins, Editions Féret, 2000. 2000Google Scholar
- Pinyopich A, Ditta GS, Savidge B, Liljegren SJ, Baumann E, Wisman E, Yanofsky MF: Assessing the redundancy of MADS-box genes during carpel and ovule development. Nature. 2003, 424 (6944): 85-88. 10.1038/nature01741.PubMedView ArticleGoogle Scholar
- Colombo L, Franken J, Van der Krol AR, Wittich PE, Dons HJM, Angenent GC: Downregulation of Ovule-Specific MADS Box Genes from Petunia Results in Maternally Controlled Defects in Seed Development. Plant Cell. 1997, 9 (5): 703-715. 10.1105/tpc.9.5.703.PubMedPubMed CentralView ArticleGoogle Scholar
- Busi M, Bustamente C, D'Angelo C, Hidalgo-Cuevas M, Boggio S, Valle E, Zabaleta E: MADS-box genes expressed during tomato seed and fruit development. Plant Mol Biol. 2003, 52: 801-815. 10.1023/A:1025001402838.PubMedView ArticleGoogle Scholar
- Dreni L, Jacchia S, Fornara F, Fornari M, Ouwerkerk PB, An G, Colombo L, Kater MM: The D-lineage MADS-box gene OsMADS13 controls ovule identity in rice. Plant J. 2007, 52 (4): 690-699. 10.1111/j.1365-313X.2007.03272.x.PubMedView ArticleGoogle Scholar
- Bailey TL, Elkan C: Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc Int Conf Intell Syst Mol Biol. 1994, 2: 28-36.PubMedGoogle Scholar
- Morgante M, Salamini F: From plant genomics to breeding practice. Curr Opin Biotechnol. 2003, 14 (2): 214-219. 10.1016/S0958-1669(03)00028-4.PubMedView ArticleGoogle Scholar
- Salvi S, Tuberosa R: To clone or not to clone plant QTLs: present and future challenges. Trends Plant Sci. 2005, 10 (6): 297-304. 10.1016/j.tplants.2005.04.008.PubMedView ArticleGoogle Scholar
- Johansen B, Pedersen LB, Skipper M, Frederiksen S: MADS-box gene evolution-structure and transcription patterns. Mol Phylogenet Evol. 2002, 23 (3): 458-480. 10.1016/S1055-7903(02)00032-5.PubMedView ArticleGoogle Scholar
- Vandenbussche M, Theissen G, Van de Peer Y, Gerats T: Structural diversification and neo-functionalization during floral MADS-box gene evolution by C-terminal frameshift mutations. Nucleic Acids Res. 2003, 31 (15): 4401-4409. 10.1093/nar/gkg642.PubMedPubMed CentralView ArticleGoogle Scholar
- Kramer EM, Su HJ, Wu CC, Hu JM: A simplified explanation for the frameshift mutation that created a novel C-terminal motif in the APETALA3 gene lineage. BMC Evol Biol. 2006, 6: 30-10.1186/1471-2148-6-30.PubMedPubMed CentralView ArticleGoogle Scholar
- Yang Y, Jack T: Defining subdomains of the K domain important for protein-protein interactions of plant MADS proteins. Plant Mol Biol. 2004, 55 (1): 45-59. 10.1007/s11103-004-0416-7.PubMedView ArticleGoogle Scholar
- Cho S, Jang S, Chae S, Chung KM, Moon YH, An G, Jang SK: Analysis of the C-terminal region of Arabidopsis thaliana APETALA1 as a transcription activation domain. Plant Mol Biol. 1999, 40 (3): 419-429. 10.1023/A:1006273127067.PubMedView ArticleGoogle Scholar
- Yalovsky S, Rodriguez-Concepcion M, Bracha K, Toledo-Ortiz G, Gruissem W: Prenylation of the floral transcription factor APETALA1 modulates its function. Plant Cell. 2000, 12 (8): 1257-1266. 10.1105/tpc.12.8.1257.PubMedPubMed CentralView ArticleGoogle Scholar
- Le Cunff L, Fournier-Level A, Laucou V, Vezzulli S, Lacombe T, Adam-Blondon AF, Boursiquot JM, This P: Construction of nested genetic core collections to optimize the exploitation fo natural diversity in Vitis vinifera L. subsp sativa. BMC Plant Biology. 2008, 8 (31)Google Scholar
- Lodhi MA, Ye GN, Weeden NF, Reisch BI: A simple and efficient method for DNA extraction from grapevine cultivars, Vitis species and Ampelopsis. Plant Mol Biol Rep. 1994, 12 (1): 6-13. 10.1007/BF02668658.View ArticleGoogle Scholar
- Acquaah G: Principles of Plant Genetics and Breeding. 2007, Blackwell PublishingGoogle Scholar
- Costantini L, Grando MS, Feingold S, Ulanovsky S, Mejia N, Hinrichsen P, Doligez A, This P, Cabezas JA, Martinez-Zapater JM: Generation of a Common Set of Mapping Markers to Assist Table Grape Breeding. Am J Enol Vitic. 2007, 58 (1): 102-111.Google Scholar
- Adam-Blondon AF, Roux C, Claux D, Butterlin G, Merdinoglu D, This P: Mapping 245 SSR markers on the Vitis vinifera genome: a tool for grape genetics. Theor Appl Genet. 2004, 109 (5): 1017-1027. 10.1007/s00122-004-1704-y.PubMedView ArticleGoogle Scholar
- Riaz S, Dangl GS, Edwards KJ, Meredith CP: A microsatellite marker based framework linkage map of Vitis vinifera L. Theor Appl Genet. 2004, 108 (5): 864-872. 10.1007/s00122-003-1488-5.PubMedView ArticleGoogle Scholar
- Lamoureux D, Bernole A, Le Clainche I, Tual S, Thareau V, Paillard S, Legeai F, Dossat C, Wincker P, Oswald M, et al: Anchoring of a large set of markers onto a BAC library for the development of a draft physical map of the grapevine genome. Theor Appl Genet. 2006, 113 (2): 344-356. 10.1007/s00122-006-0301-7.PubMedView ArticleGoogle Scholar
- Temnykh S, DeClerck G, Lukashova A, Lipovich L, Cartinhour S, McCouch S: Computational and experimental analysis of microsatellites in rice (Oryza sativa L.): frequency, length variation, transposon associations, and genetic marker potential. Genome Res. 2001, 11 (8): 1441-1452. 10.1101/gr.184001.PubMedPubMed CentralView ArticleGoogle Scholar
- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol. 1990, 215 (3): 403-410.PubMedView ArticleGoogle Scholar
- Rozen S, Skaletsky H: Primer3 on the WWW for general users and for biologist programmers. Methods Mol Biol. 2000, 132: 365-386.PubMedGoogle Scholar
- Iniciativa Genoma Chile, Plataforma científica-tecnológica para el desarrollo de la genómica en Chile. http://www.genomicafrutos.cl/, [http://genomicavitis.dim.uchile.cl/]
- Creste S, Tulmann A, Figueira A: Detection of Single Sequence Repeat Polymorphisms in Denaturing Polyacrylamide Sequencing Gels by Silver Staining. Plant Mol Biol Rep. 2001, 19: 299-306. 10.1007/BF02772828.View ArticleGoogle Scholar
- Martins-Lopes P, Zhang H, Koebner R: Detection of Single Nucleotide Mutations in Wheat Using Single Strand Conformation Polymorphism Gels. Plant Mol Biol Rep. 2001, 19: 159-162. 10.1007/BF02772158.View ArticleGoogle Scholar
- Maliepaard C, Jansen J, Van Ooijen JW: Linkage analysis in a full-sib family of an outbreeding species: overview and consequences for applications. Genet Res Camb. 1997, 70: 237-250. 10.1017/S0016672397003005.View ArticleGoogle Scholar
- Grattapaglia D, Sederoff R: Genetic linkage maps of Eucalyptus grandis and Eucalyptus urophylla using a pseudo-testcross: mapping strategy and RAPD markers. Genetics. 1994, 137 (4): 1121-1137.PubMedPubMed CentralGoogle Scholar
- Van Ooijen JW, Voorrips RE: JOINMAP 3.0, software for the calculation of genetic linkage maps. Plant Research International, Wageningen, Netherlands. 2001Google Scholar
- Kosambi DD: The estimation of map distances from recombination values. Ann Eugen. 1944, 12: 172-175.View ArticleGoogle Scholar
- International Grape Genome Program. http://www.vitaceae.org/index.php/International_Grape_Genome_Program, [http://www.vitaceae.org/]
- Lander ES, Botstein D: Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics. 1989, 121 (1): 185-199.PubMedPubMed CentralGoogle Scholar
- Van Ooijen JW, Boer MP, J RC, Maliepaard C: MapQTL 4.0, software for the calculation of QTL positions on genetic maps. Plant Research International, Wageningen, Netherlands. 2002Google Scholar
- Doerge RW, Churchill GA: Permutation tests for multiple loci affecting a quantitative character. Genetics. 1996, 142 (1): 285-294.PubMedPubMed CentralGoogle Scholar
- Untergasser A, Nijveen H, Rao X, Bisseling T, Geurts R, Leunissen JA: Primer3Plus, an enhanced web interface to Primer3. Nucleic Acids Res. 2007, W71-74. 10.1093/nar/gkm306. 35 Web ServerPubMedPubMed CentralView ArticleGoogle Scholar
- Drummond AJ, Ashton B, Cheung M, Heled J, Kearse M, Moir R, Stones-Havas S, Thierer T, Wilson A: Geneious v4.0. 2008, [http://www.geneious.com/]Google Scholar
- Baggiolini M: Les stades repères dans le développement annuel de la vigne et leur utilisation pratique. Rev Romande Agric Vitic Arboric. 1952, 8: 4-6.Google Scholar